Polysomnograms are labor-intensive, expensive and impractical to perform repeatedly to assess therapeutic efficacy. Typical measures of sleep disordered breathing (SDB) severity include event counts, such as the apnea-hypopnea index, that utilize physiologically arbitrary scoring criteria. These measures correlate only modestly with neurobehavioral and cardiovascular outcomes in adults and children. Standard sleep stage scoring differentiates REM and NREM sleep types and NREM depth, but provides little information about physiological stability. Thus, there is a need for a simple, inexpensive and easily repeatable measure of the presence and impact of SDB on sleep state physiology and stability. The investigators have developed a fully automated surface electrocardiogram (ECG)-derived sleep physiology estimator, based on the quantitative analysis of cardiopulmonary coupling from a single-lead ECG. This approach combines the use of mechanical and autonomic effects of physiologic vs. periodic breathing on ECG parameters related to heart rate variability and QRS electrical axis. Physiologically unstable sleep associated with SDB manifests the EEG morphology of cyclic alternating pattern (CAP), regardless of conventional stage scoring. In contrast, periods of non-CAP represent physiologically stable sleep behavior. We have found that the coupling estimator shows a strong correlation with CAP and non- CAP states, thus providing a promising new biomarker of sleep physiology and pathology based on the percentage of sleep spent in periods of unstable sleep behavior. The goals of the proposed research are to: 1) Refine the technique using a sleep laboratory database. 2) Test it, using established databases, as a SDE detector in community-dwelling adults (Sleep Heart Health Study, SHHS) and children (Neurobehavioral Assessment and Pediatric Sleep, NAPS). 3) To correlate the biomarker with excessive daytime sleepiness, subjective sleep quality and cardiovascular morbidity (SHHS), and reduced neurocognitive function (NAPS).